Politicians, financial managers, attorneys, and all manner of individuals, both professional and private, have long been concerned with, and interested in, obtaining and discussing probabilities relating to the outcome of real-world events. See, e.g., James Surowiecki, Wisdom of the Crowds (2004). Past approaches to collecting and publishing such collective intelligence has suffered from a variety of shortcomings and resulted in difficulties with both the collecting of prediction data and ensuring the accuracy and quality of the data derived from the collected prediction data.
One classic approach to collecting predictions from individuals has been through the use of polling procedures, and in particular, face-to-face polling. Such an approach is limiting in that its scalability is a function of the number of available pollsters at a given time and in a given location. Similarly, it is dependent on the willingness and availability of a polling subject to participate in the polling activity at that same given time and given location. This has resulted in simplified survey questions and shortened overall surveys.
Mail-based surveys have attempted to address this limitation by providing the polling subject with the ability to answer polling questions at a time convenient for the subject. While this partially addressed the issue of convenience, there is typically very little motivation for the subject to go to the trouble of answering questions and returning the answers to the pollster. Telephone solicitation has attempted to reintroduce the pressure of direct communication, but this approach suffers the disadvantages too, such as annoying subjects with unexpected interruptions, resulting in limited response volume.
With the advent of the Internet long ago, it has long been possible to reach mass numbers of potential subjects in a less-intrusive manner than through direct interaction. Initially, polling was provided through email and email widgets. While this can be initially entertaining to email users, it often quickly becomes an annoyance, suffering the same dismissive fate of physical mail surveys.
Online surveys that displayed basic results, particularly within the context of a social network application or service such as Facebook®, were introduced relatively long ago as yet another way to try and entice individuals into participating in surveys and polls. These polls are typically very simple and short, as, again, there is little motivation for the subject to invest time and thought into engaging in trying to provide accurate information. In this context, polling tends to be extremely simple, and the prediction information produced is limited in terms of depth, accuracy and reliability.
Commercial prediction markets also have emerged, in part, as an attempt to address the lack of motivation for individuals to participate in thoughtful prediction behaviors. Many of these systems consist of a speculative market where the current market price of a prediction is interpreted as the probability of an event occurring in the future. The use of real money in these commercial markets has run afoul of various laws and regulations, resulting in such systems being entirely banned in at least some countries. In addition, the focus on trading activity rather than improving the accuracy and precision of the information derived resulted in the introduction of bias and sub-optimal derivation data.
In an effort to remove the commercial bias from the data collection activities, virtual prediction markets also have been introduced. These systems tend to be narrow in their focus and traditionally are relegated to specific niche markets. They are limited in the types of predictions they can host and manage, lack the necessary incentives to encourage lasting engagement by users, and generally offer no way for end users or groups to create their own private solutions without the help of costly third-party consulting. In addition, these systems also have typically required post-collection consulting services for data interpretation and relied on ad-hoc settlement methods, making it time consuming and costly to understand the resulting data and to extend the system to new markets.
Gamification of virtual prediction markets has been used as a mechanism to try and increase interest and participation in the use of such systems. These gamified systems tend to be very simplistic with extremely limited types of predictions and with a focus tending towards the game aspects of the system. They have typically adopted the trading model, suffering from the same disadvantages of the commercial predictive market solutions mentioned previously. The simplistic systems, along with the absence of broad and customizable integration with external services such as blogs, feed readers, search engines, and analytics systems, have not provided sufficient motivation for extended high-volume participation. As a result, there has been a lack of sufficient participation to generate useful aggregated collective probabilities, a lack of demand for the publication of such data, and therefore an inability to substantially monetize data publication.
Further, prior prediction generating or gathering systems are typically rigidly structured by the system provider. They have provided little if any ability for end users or other users of the systems to provide input into establishing a prediction generating, gathering, or reporting program.